Transcript Semantic and Streaming Grids Chinese Academy of Sciences Dec 6 2005 Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 [email protected] http://www.infomall.org.

Semantic and Streaming
Grids
Chinese Academy of Sciences Dec 6 2005
Geoffrey Fox
Computer Science, Informatics, Physics
Pervasive Technology Laboratories
Indiana University Bloomington IN 47401
[email protected]
http://www.infomall.org
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Four Data Streaming Application Areas
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Data Assimilation applied to link the data deluge
(satellites, sensors, seismometers) in real time to small
and large scale parallel simulations
• Use in Earthquake Science
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Department of Defense (and Homeland Security) have
built the Global Information Grid with a target
architecture NCOW (Network Centric Operations and
warfare)
• They submit no jobs; rather stream data to brokers from
which they are filtered and distributed
• Includes their rather dated distributed simulation HLA
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Audio-Video Conferencing implemented with services
and Grid messaging
Hand-held Grid linking PDA/cell-phones to Grids
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Data Deluged Science
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In the past, we worried about data in the form of parallel I/O or
MPI-IO, but we didn’t consider it as an enabler of new science
and new ways of computing
Data assimilation was not central to HPCC
DoE ASCI set up because didn’t want test data!
Now particle physics will get 100 petabytes from CERN
• Nuclear physics (Jefferson Lab) in same situation
• Use around 30,000 CPU’s simultaneously 24X7
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Weather, climate, solid earth (EarthScope)
Bioinformatics curated databases (Biocomplexity only 1000’s of
data points at present)
Virtual Observatory and SkyServer in Astronomy
Environmental Sensor nets
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Information/Knowledge Grids
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Distributed (10’s to 1000’s) of data sources (instruments,
file systems, curated databases …)
Data Deluge: 1 (now) to 100’s petabytes/year (2012)
• Moore’s law for Sensors
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Possible filters assigned dynamically (on-demand)
• Run image processing algorithm on telescope image
• Run Gene sequencing algorithm on compiled data
Needs decision support front end with “what-if”
simulations
Metadata (provenance)
critical to annotate data
Integrate across experiments
as in multi-wavelength
astronomy
Data Deluge comes from pixels/year available
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Raw Data 
S
S
S
S
FS
FS
FS
FS
MD
FS
MD
O
S
FS
O
S
FS
F
S
FS
MD
MD
SS
O
S
FS
FS
O
S
FS
MD
O
S
FS
F
S
O
S
MD
Filter Service
FS
O
S
FS
Other
Service
MD
O
S
FS
MetaData
SS
S
S
Database
O
S
FS
SS
Another
Grid
FS
O
S
O
S
SS
Decisions
MD
MD
FS
SS
FS
S
S
O
S
SS
Another
Service
 Wisdom
Knowledge
Another
Grid
FS
SS
Information 
S
S
Another
Grid
Data 
S
S
S
S
Another
Service
S
S
S
S
S
S
S
S
S
S
S
S
Sensor Service
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Semantic Grid and Services
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Implications of SOA (Service Oriented Architectures) for SG
(Semantic Grid)
• Build services to implement SG
Implications of SG for SOA
• Build metadata rich systems of services using SG
Services receive data in SOAP messages, manipulate it and
produce transformed data as further messages
Meta-data is carried in SOAP messages
Meta-data controls processing and transport of SOAP Messages
Knowledge is created from data by services
The Grid enhances Web services with semantically rich system
and application specific management
One must exploit and work around the different approaches to
meta-data and their manipulation in Web Services
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Structure of SOAP Messages
Container Workflow
H1
H2
H3
H4
Body
F1
F2
F3
F4
Service
Container Handlers
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SOAP Messages have System information in the header
including WS-Policy based meta-data defining processing
options
• Processed by Handlers
Application data and meta-data is the body (controversies here!)
• Processed by the Service itself
Some meta-data like WS-RF is logically “only in messages”
Other like that in WS-Context or the SRB are stored in logical
equivalent of XML databases
We only need to preserve semantic structure (XML/SOAP
Infoset) so transport in fast XML and store in efficient relational
databases
7
What Type of Services are there?
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There are a horde of support services supplying security,
collaboration, database access, user interfaces
The support services are either associated with system or
application
• We will study the WS-* and GS-* which implicitly or
explicitly define many support services
There are generalized filter services which are applications that
accept messages and produce new messages with some data
derived from that in input
• Simulations (including PDE’s and reactive systems)
• Data-mining
• Transformations
• Agents
• Reasoning
are all termed filters here
There are services like “author ontology”, “parse RDF” or
“attach provenance” that directly support Semantic Grid
But all services and their interactions are bathed in sea of metadata and so implicitly need and support the Semantic Grid
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It’s a Composite Hierarchical World
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Filters can be a workflow which means they are “just collections
of other simpler services”
• One needs meta-data to control the workflow
Services are programs that accept messages and produce
messages
Grids are a distributed collection of services supporting
managed shared resources
• Management requires meta-data
Grids are distributed systems that accept distributed messages
and produce distributed result messages
• Can always talk about Grids and view a service or a
workflow as a special case of a Grid
It just requires meta-data to send a message to a Grid and it
routed to “correct computer” holding “requested service”
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• Meta-data allows mapping of virtual to real addresses
Semantically Rich Services with a Semantically
Rich Distributed Operating Environment
O
SOAP Message
Streams
SS
S
Another
Service
Filter Service
FS
Wisdom
MD
Data
FS
SS
Raw Data
FS
Data
FS
Raw Data
O
S
O
FS
Knowledge
S
O
S
MD
Information
FS
MD
SS
FS
SS
FS
O
S
FS
FS
FS
MD
F
S
MD
Knowledge
O
S
MD
F
S
Information
O
S
O
S
FS
Other
Service
MD
O
S
DataFS
FS
O
S
FS
MD
Data
FS
Decisions
O
S
FS
FS
SS
SS
MD
O
S Information FS
SS
Another
Service
FS
MetaData
SS
S
S
Another
Database
Grid
S
S
Raw Data
S
S
S
S
Grids of Grids Architecture
S
S
S
S
S
S
S
S
Raw Data
SOAP
Message Streams
Another
Grid
S
S
Sensor Service
is same as outward
facing application
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service
GIS Grids and Sensor Grids
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OGC has defined a suite of data structures and services
to support Geographical Information Systems and
Sensors
GML Geography Markup language defines
specification of geo-referenced data
SensorML and O&M (Observation and Measurements)
define meta-data and data structure for sensors
Services like Web Map Service, Web Feature Service,
Sensor Collection Service define services interfaces to
access GIS and sensor information
Grid workflow links services that are designed to
support streaming input and output messages
We are building Grid (Web) service implementations of
these specifications for NASA’s SERVOGrid
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A Screen Shot From the WMS Client
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WMS uses WFS that uses data sources
<gml:featureMember>
<fault>
<name> Northridge2 </name>
<segment> Northridge2
</segment>
<author> Wald D. J.</author>
<gml:lineStringProperty>
<gml:LineString
srsName="null">
<gml:coordinates>
-118.72,34.243 118.591,34.176
</gml:coordinates>
</gml:LineString>
</gml:lineStringProperty>
</fault>
</gml:featureMember>
`
WMS
le
ec
tio
n
Fe
a
ol
tur
eC
eC
oll
Ge
tF
ea
e
r
tu
r
tu
a
Fe
a
Fe
et
G
tur
e
Client
io
ct
n
s
ad
i l ro ]
a
R [a-b
Railroads
WFS Server
Hi
River [a-d]
Bridge [1-5]
ry
SQL Query
ue
LQ
SQ
SQ
L
gw
ay
[1
2-
Q
ue
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ry
]
Interstate
Highways
Rivers
Bridges
90
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Electric Power and Natural Gas data from LANL
Interdependent Critical Infrastructure Simulations
Zoom-in
Zoom-out
FeatureInfo mode
Measure distance mode
Clear Distance
Drag and Drop mode
Refresh to initial map
Typical use of Grid Messaging in NASA
Sensor Grid
Grid Eventing
Datamining Grid
GIS Grid
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Typical use of Grid Messaging
Filter or
Datamining
Sensor Grid
Post after
Processing
Post before
Processing
Narada
Brokering
Grid Database
Archives
Web Feature Service
Notify
Subscribe
HPSearch
Manages
WS-Context
Stores dynamic data
WFS
(GIS data)
GIS Grid
Geographical
Information System
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Real Time GPS
and Google Maps
Subscribe to live GPS
station. Position data
from SOPAC is
combined with Google
map clients.
Select and zoom to
GPS station location,
click icons for more
information.
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Google maps
can be
integrated with
Web Feature
Service
Archives to
filter and
browse seismic
records.
Integrating
Archived Web
Feature Services
and Google Maps
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Google Maps
as Service
accessed from
our WMS
Client
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3 XML Databases of Importance
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WS-Context controlling a workflow
(Extended) UDDI supporting semantic service discovery
WFS or ASFS (see later) provides application specific
data/meta-data repository)
These have different performance, scalability and data unit size
requirement
In our implementation, each is currently “just an
Oracle/MySQL” database front ended by filters that convert
between XML (GML for WFS) and object-relational Schema
• Example of Semantics (XML) versus representation (SQL)
difference
OGSA-DAI offers Grid interface to databases – we could use but
don’t as we only need to expose WFS and not MySQL to Grid
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Information Management/Processing
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SOAP messages transport information expressed in a
semantically rich fashion between sources and services that
enhance and transform information so that complete system
provides
• Semantic Web technologies like RDF and OWL help us have
rich expressivity
Data  Information  Knowledge transformation
We build application specific information
management/transformation systems ASIS for each application
domain
One special domain is the system itself where the metadata
associated with services, sessions, Grids, messages, streams and
workflow is itself managed and supported by an SIIS
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Generalizing a GIS
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Geographical Information Systems GIS have been
hugely successful in all fields that study the earth and
related worlds
• They define Geography Syntax (GML) and ways to store,
access, query, manipulate and display geographical features
• In SOA, GIS corresponds to a domain specific XML language
and a suite of services for different functions above
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However such a universal information model has not
been developed in other areas even though there are
many fields in which it appears possible
•
•
•
•
•
BIS Biological Information System
MIS Military Information System
IRIS Information Retrieval Information System
PAIS Physics Analysis Information System
SIIS Service Infrastructure Information System
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ASIS Application Specific Information System I
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a) Discovery capabilities that are best done using WS-*
standards
b) Domain specific metadata and data including
search/store/access interface. (cf WFS). Lets call generalization
ASFS (Application Specific Feature Service)
• Language to express domain specific features (cf GML). Lets call
this ASL (Application Specific language)
• Tools to manipulate information expressed in language and key
data of application (cf coordinate transformations). Lets call this
ASTT (Application specific Tools and Transformations)
• ASL must support Data sources such as sensors (cf OGC metadata
and data sensor standards) and repositories. Sensors need
(common across applications) support of streams of data
• Queries need to support archived (find all relevant data in past)
and streaming (find all data in future with given properties)
• Note all AS Services behave like Sensors and all sensors are
wrapped as services
• Any domain will have “raw data” (binary) and that which has been
filtered to ASL. Lets call ASBD (Application Specific Binary Data)
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ASIS Application Specific Information System II
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Lets call this ASVS (Application Specific Visualization Services)
generalizing WMS for GIS
The ASVS should both visualize information and provide a way of
navigating (cf GetFeatureInfo) database (the ASFS)
The ASVS can itself be federated and presents an ASFS output interface
d) There should be application service interface for ASIS from which all
ASIS service inherit
e) There will be other user services interfacing to ASIS
All user and system services will input and output data in ASL using
filters to cope with ASBD
AS
Repository
Filter, Transformation, Reasoning,
Data-mining, Analysis
AS Tool
(generic)
AS Service
(user defined)
AS Tool
(generic)
ASVS
Display
AS
“Sensor”
Messages using ASL
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Directly GS-* WS-*
Filters/ASTT
Military
Information
Management
System
Everything
Is a
Service
or a message/
Information
Nugget
ASVS
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MIO
or Military
Information
Object
ASFS
Unit of
Managed
Information
expressed in
ASL
OGSA-DAI and Sensor Standards
Info-D
WS-Notification
WS-Eventing
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Two-level Programming I
• The Web Service (Grid) paradigm implicitly assumes a
two-level Programming Model
• We make a Service (same as a “distributed object” or
“computer program” running on a remote computer) using
conventional technologies
– C++ Java or Fortran Monte Carlo module
– Data streaming from a sensor or Satellite
– Specialized (JDBC) database access
• Such services accept and produce data from users files and
databases
Service
Data
• The Grid is built by coordinating such services assuming
we have solved problem of programming the service 27
Two-level Programming II
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The Grid is discussing the composition of distributed
services with the runtime Service1
Service2
interfaces to Grid as
opposed to UNIX
Service3
Service4
pipes/data streams
Familiar from use of UNIX Shell, PERL or Python
scripts to produce real applications from core programs
Such interpretative environments are the single
processor analog of Grid Programming
Some projects like GrADS from Rice University are
looking at integration between service and composition
levels but dominant effort looks at each level separately
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3 Layer Programming Model
Web Service 1
WS 2
WS N-1
Web Service N
Level 1 Programming inside services
Application expressed in in Java Fortran C++ MPI etc.
WS-* Infrastructure
Level 2 Programming choosing services by virtualization
Application Semantics (Metadata, Ontology) Semantic Grid
Level 3 Grid Programming composing multiple services
Service Workflow, Transactions, Mediation
Substantial work in UK e-Science program,
international semantic web community
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Consequences of Rule of the Millisecond
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Useful to remember critical time scales
•
•
•
•
•
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Classic
1) 0.000001 ms
– CPU does a calculation
Programming
2a) 0.001 to 0.01 ms – Parallel Computing MPI latency
2b) 0.001 to 0.01 ms – Overhead of a Method Call
3) 1 ms
– wake-up a thread or process
either?
4) 10 to 1000 ms – Internet delay:
Workflow
So use pointers and the compute memory system when
latencies of ≤ 1 millisecond but use URI looked up in a
context store when longer delays allowed
Transfer data when read-only and long latency allowed
Always choose the slowest allowed methodology and
remember when in doubt, Moore’s law favors computer
performance and systems always get more complex and
harder to maintain.
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GlobalMMCS Web Service Architecture
Use Multiple Media servers to scale to many codecs and many
versions of audio/video mixing
Session Server
XGSP-based Control
NaradaBrokering
All Messaging
NB Scales as
distributed
Admire
Web
Services
SIP
H323
Media Servers
Filters
High Performance (RTP)
and XML/SOAP and ..
Access Grid
Native XGSP
Gateways convert to uniform XGSP Messaging
NaradaBrokering
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GlobalMMCS Architecture
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Non-WS collaboration control protocols are
“gatewayed” to XGSP
NaradaBrokering supports TCP (chat, control, shared
display, PowerPoint etc.) and UDP (Audio-Video
conferencing)
Audio Video
Web Service
Instant
Messaging
Shared
Display
Shared
Web Service
Web Service
Web Service
….
XGSP Conference Control Service
Event Messaging Service
(NaradaBrokering)
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XGSP Example: New Session
<CreateAppSession>
<ConferenceID> GameRoom </ConferenceID>
<ApplicationID> chess </ApplicationID>
<AppSessionID> chess-0 </AppSessionID>
<AppSession-Creator> John </AppSession-Creator>
<Private> false </Private>
</CreateAppSession>
<SetAppRole>
<AppSessionID> chess-0 </AppSessionID>
<UserID> Bob </UserID>
<RoleDescription> black </RoleDescription>
</SetAppRole>
<SetAppRole>
<AppSessionID> chess-0 </AppSessionID>
<UserID> Jack </UserID>
<RoleDescription> white </RoleDescription>
</SetAppRole>
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XGSP AV Signaling Protocol with H.323
H323 Terminal
H323
H323 Gateway
H225.Setup
H.225
Call Setup
H225.Connect
XGSP
Session server
JoinAVSession
JoinAVSession OK
with the RTPLinks <IP Addr, Port>
H.245
Capability
Exchange
Terminal Capability
Set
ACK
& capability description
Terminal Capability Set
ACK
Open
Audio &
Video
Logic
Channels
OpenLogicChannel ( Video )
ACK
OpenLogicChannel ( Video )
ACK
ACK with video RTPLink
<IP Addr, Port>
JoinAVSession (Video)
OpenLogicChannel ( Audio )
ACK
OpenLogicChannel ( Audio )
ACK
ACK with Audio RTPLink
<IP Addr, Port>
JoinAVSession
(Audio)
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NaradaBrokering 2003-2006
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Messaging infrastructure for collaboration, peer-to-peer and Grids
Implements JMS and native high-performance protocols (message
transit time of 1 to 2 ms per hop)
Order-preserving message transport with QoS and security profiles
Support for different underlying transport such as TCP, UDP,
Multicast, RTP
SOAP message support and WS-Eventing, WS-RM and WS-Reliability.
• WS-Notification when specification agreed
Active replay support: Pause and Replay live streams.
Stream Linkage: can link permanently multiple streams – using in
annotation of real-time video streams
Replicated storage support for fault tolerance and resiliency to storage
failures.
Management: HPSearch Scripting Interface to streams and brokers
(uses WS-Management)
Broker Topics and Message Discovery: Locate appropriate
Integration with Axis2 Web Service Container (?)
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High Performance Transport supporting SOAP Infoset
Average Video Delays for one broker –
Performance scales proportional to number of brokers
Latency ms
Multiple
sessions
One session
30 frames/sec
# Receivers
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Collaboration Grid
WS-Context
HPSearch
UDDI
Narada
Broker
Audio Mixer
Video Mixer
Narada
Broker
WS-Security
Gateway
XGSP Media
Service
Narada
Broker
Gateway
SharedWS
Transcoder
Thumbnail
Replay
Record
Annotate
SharedDisplay
WhiteBoard
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GIS
TV
Chat
Video Mixer
Webcam
GlobalMMCS SWT Client
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e-Annotation
e - Annotation
Player
Player
Archived
Archieved
stream list
Stream List
Archived Stream
Archived stream
Player
player
Real time
Real
Time
stream list
Stream List
Annotated
Annotation
/WB
Stream
Player
player
Real time stream
player
e-Annotation
e -Annotation
Whiteboard
Whiteboard
Real Time
Player
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Location of software for Grid Projects in
Community Grids Laboratory
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htpp://www.naradabrokering.org provides Web service
(and JMS) compliant distributed publish-subscribe
messaging (software overlay network)
htpp://www.globlmmcs.org is a service oriented (Grid)
collaboration environment (audio-video conferencing)
http://www.crisisgrid.org is an OGC (open geospatial
consortium) Geographical Information System (GIS)
compliant GIS and Sensor Grid (with POLIS center)
http://www.opengrids.org has WS-Context, Extended
UDDI etc.
The work is still in progress but core part of
NaradaBrokering is quite mature
All software is open source and freely available
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Summary
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Virtualization everywhere
Focus on semantics not representation to get
performance combined with expressivity for transport
and data access
All this enabled by powerful meta-data services
Grids add management to rich but potentially chaotic
set of Web Services;
• management and coherence enabled by meta-data
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Can define general information architectures (ASIS,
GIS, SIIS) for both applications and system
Knowledge from filters that span simulations, datamining, reasoning and agents
A service is just a special case of a Grid
Build systems from SubGrids (Gridlets)
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